To summarize, statistical models can address stochastic positivity violations under additional model-form assumptions. When \(\mathbf {Z}\)  is high-dimensional, the model specification assumption becomes a remedy to ubiquitous stochastic positivity violations. While data-adaptive algorithms may impose fewer constraints than standard parametric models, constraints become necessary in high-dimensional settings.[1]}, [2]}
